Why This Job is Featured on The SaaS Jobs
This Sr. Staff AI/ML Engineer role stands out in the SaaS landscape because it is centered on building internal AI platform primitives that many product surfaces can reuse. Rather than shipping a single feature, the work focuses on agent orchestration, retrieval infrastructure, evaluation tooling, model serving, and safety guardrails that enable multiple teams to deliver AI-powered experiences with consistency and control.
From a SaaS career perspective, platform ownership at this level compounds. Experience designing shared capabilities, defining standards, and operating production ML systems maps directly to how mature SaaS companies scale product development across many squads. The emphasis on observability, evaluation, and lifecycle management also reflects where SaaS AI is heading: measurable quality, dependable deployment, and repeatable patterns that reduce time-to-production for downstream teams.
This role fits an engineer who prefers leverage over locality, enjoys turning ambiguous AI needs into durable abstractions, and is comfortable influencing architecture across organizational boundaries. It will resonate most with someone motivated by reliability and adoption as success metrics, and who wants to mentor while remaining deeply hands-on with modern LLM and platform engineering tradeoffs.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role:
- You'll design, build, and scale the AI platform that empowers Gusto's internal teams to build intelligent agents and deliver customer value. This includes developing core platform capabilities — agent orchestration, RAG infrastructure, eval tooling, model serving, prompt management, observability, and safety guardrails — that enable product teams to rapidly build, test, and ship AI-powered experiences. You'll own the full lifecycle from problem framing to production deployment, ensuring the platform is reliable, performant, and easy to adopt. As a Staff MLE, you'll also shape technical standards, drive architectural decisions, and mentor engineers across the organization.
About the Team:
- You'll join the CoreX AI Platform team, responsible for building the foundational AI infrastructure that Gusto's internal app teams rely on to create and ship intelligent agents. We're a small, high-impact group of ML engineers and platform builders working closely with Product Engineers, PMs, and Designers across the company. We move fast, prototype boldly, and focus on making it easy for any team a
What you'll do day-to-day:
- Design and build scalable platform services — agent orchestration, RAG pipelines, eval frameworks, model serving — that internal teams use to ship AI-powered products.
- Lead technical strategy and architecture for the AI platform, including model lifecycle, observability, safety guardrails, and evaluation infrastructure.
- Collaborate cross-functionally with app teams, PMs, and Designers to understand their AI needs and deliver platform capabilities that unblock them.
- Build, harden, and operate shared infrastructure that powers intelligent agents across Gusto (e.g., retrieval, routing, prompt management, content selection).
- Stay current with AI/ML research; rapidly prototype and productionize new techniques that strengthen the platform.
- Establish robust practices for validation, deployment, monitoring, and ongoing performance management of platform services and the agents built on them.
- Communicate technical strategy, tradeoffs, and impact clearly to executives and non-technical partners.
- Mentor engineers across teams, raising the bar on AI/ML best practices and fostering a culture of pragmatic innovation.
Here’s what we’re looking for
- 12+ years building and deploying end-to-end AI/ML systems, with experience designing platform-level infrastructure that other engineering teams build on.
- Deep expertise in one or more areas: LLMs, NLP, retrieval/RAG, agent orchestration, deep learning, or reinforcement learning.
- Hands-on experience building LLM-based applications and agentic workflows — including prompt engineering, retrieval design, evaluation, and production deployment.
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) and cloud platforms (GCP, AWS, or Azure).
- Strong Python skills and sound software engineering fundamentals — testing, code review, CI/CD, reliability, and API design.
- Proven track record shipping impactful AI/ML projects to production, ideally in a platform or infrastructure context.
- Demonstrated ability to lead cross-functionally, influence technical direction, and communicate clearly with both engineers and non-technical stakeholders.
- Ph.D. or Master's in CS, ML, Statistics, Mathematics, or related field is a plus.
Compensation
Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.
